1Section of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA2Center for Computational Biology and Bioinformatics, The University of Texas at Austin, Austin, TX 78712, USA3Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX 78712, USA

Abstract

The inference of population dynamics from molecular sequence data
is becoming an important new method for the surveillance of infectious
diseases. Here, we examine how heterogeneity in contact shapes the
genealogies of parasitic agents. Using extensive simulations, we find
that contact heterogeneity can have a strong effect on how the structure
of genealogies reflects epidemiologically relevant quantities such as the
proportion of a population that is infected. Comparing the simulations
to BEAST reconstructions, we also find that contact heterogeneity can
increase the number of sequence isolates required to estimate these
quantities over the course of an epidemic. Our results suggest that
data about contact-network structure will be required in addition to
sequence data for accurate estimation of a parasitic agent's genealogy.
We conclude that network models will be important for progress in
this area.